Personalized recommendations received from YouTube and Netflix, Smart Home Assistance, Voice Recognition, Conversational chatbots, etc., are the greatest examples of Machine Learning technology. Machine learning is the sub-branch of Artificial Intelligence and one of the technologies that have grown in popularity with time. Whether you have associated with the IT industry or not, machine learning and artificial intelligence are no more buzz words.
Machine learning has always been one of the most innovative topics in the tech industry. Right now, big data, automatic cars, and NLP are dominating the industries, and their significance peaked in 2021, and we are anticipating much more of them in the coming years.
Machine learning technology and its algorithms have already found their space everywhere, from eCommerce to healthcare to digital wallet to the retail industry to smart home assistance; machine learning is everywhere as it holds the potential to come up with cutting-edge technology solutions for organizations.
Even one of Forbes research projected that the global machine learning market would expand to more than $30.6 billion by 2024. Considering those figures, we have come with trends that will dominate the machine learning market in 2021 and beyond.
Emerging Machine Learning Trends to Watch Out in 2021
Organizations are going digital in order to bring more revenue and customers to this highly volatile market. The word “data” is a new power, and brands across the globe are trying to filter and sort data to cater to improved user experience. Whether that brand is:
- Business analytics
- FinTech sectors
- Or government agencies
Different industries are experimenting with machine learning technology. So let’s see some of the upcoming ML trends that may change the future. Here we have summarized the top machine learning trends shared by industry experts.
Automated Machine Learning
Automation of machine learning (AutoML) is emerging as a new area that can be used for both research and business applications as it has the potential to simplify the process, reduce the time required to create a model, as well as improve performance.
Machine learning can be applied to real-world problems in a variety of ways — from acquiring data to analyzing prediction models. It is undoubtedly feasible to automate all the steps while also providing a fully optimized model and prepared for prediction. AutoML aims to automate this end-to-end process.
Automated machine learning also helps in using machine learning best tools and practices that save time and resources. Here you can consider the example of Microsoft Azure that helps you build and deploy predictive models quickly.
Hyper Automation and Machine Learning
Hyper Automation is the emerging technology introduced by Gartner where brands and organizations will quickly identify and automate complex business tasks. Gartner also anticipates that by 2024, brands will experience a sudden reduction in operating costs by 30% with the implementation of hyper-automation techniques and redesigned functional methods.
Earlier, hyper-automation was not that popular, but the corona pandemic has pushed the adoption of this emerging technology where AI and ML techniques automate several business tasks and acts as major drivers.
No, this technology is not only limited to tasks that are associated with robotic process automation. In fact, it is the first step towards the implementation of this kind of technology solution. Indeed, ML and AI are important components of hyper-automation.
In order to get effectiveness in the organization, hyper-automation processes and tools can’t rely on single software solutions; they must have had the ability to adapt to changing conditions and respond to unimaginable situations.
Usage of ML for Cybersecurity Applications
The usage of machine learning and its algorithms keeps increasing across multiple industries, and one of such industries is cybersecurity applications. From antivirus software to combat cyber-crime to identity threats before it actually takes place, machine learning offers many applications to this particular industry.
AI and ML are helping organizations and brands to supercharge their cloud migration strategy and improve the performance of other associated technologies such as big data, AI, and predictive analysis. It is predicted that the usage of AI and ML in the cybersecurity domain will soon surpass $38.2 billion by 2026.
Cybersecurity consists of lots of data points, and AI clarifies, processes, and filters it with advanced algorithms while ML here analyzes the past data and comes up with the solutions that can be used in the future. Based on the previous data, the system will provide suggestions on various patterns to prevent threats and malware.
FinTech startups and banking institutions using ML technology that adds multiple layers to their systems, identify threats, and automate complicated tasks. Along with this, machine learning can also be used to mitigate breaches and allow brands to respond to cyber-attacks without human assistance.
Ml for Effective Marketing
Marketing dynamics keep evolving with customer’s preferences and innovation in technologies. If businesses want to survive in the cut-throat competitive market, they need to adopt ongoing marketing trends such as social media, SEO, personalized email marketing techniques, and much more.
It would become quite easy for marketers to formulate the business strategies if they previously analyzed the user’s behavior and patterns. To identify users’ preferences, machine learning can be used to mine data and come up with solutions that bring positive results.
In short, the adoption of machine learning technology will benefit the marketing arena and help marketers to go more personalized with customers as it was never before.
Synchronization of Machine Learning and IoT
The Internet of Things, referred to as IoT, is a technology that is no longer in its infant stage. It is a developed technology that establishes the connection between certain devices and appliances across a network. Today, the business world is experiencing various IoT effects that have not been contemplated earlier. Each of these connections has the ability to interact with each other. The adoption of IoT is increasing across certain industries, and there are chances that by 2025, more than 64 billion IoT devices will be there.
The main purpose of these connected devices is to collect information that can be evaluated and processed to make informed decisions. Here machine learning proves to be very handy as it can be used to transform the data collected from various devices into meaningful, actionable results.
For example, within an industrial setting, IoT networks all around a facility can collect operational and performance data, which is later analyzed by AI systems to improve and enhance the production capacity, boost effectiveness, and predict when machines will need maintenance.
The Future of ML: It is Only Getting Started
Entered into the new decade, we hope that the implementation of machine learning trends disrupts various industries. Several companies have already integrated machine learning into their business processes, and these trends are predicted to grow significantly in the coming years.
AI/ML implementation will benefit an organization the most if it takes advantage of the latest research and trends to develop and implement the next best solution.
Brijesh Vadukiya is a tech activist and avid blogger. My major concern is to educate people who are interested in technology. I am passionate about helping people in all aspects of SaaS solutions, online delivery business, digital marketing and other related topics that make tomorrow’s world better. I am fond of writing useful and informative content that helps brands to grow business.